Artificial Intelligence Can Improve Patient Management at the Time of a Pandemic: The Role of Voice Technology PMC

Healthcare providers can overcome this challenge by investing in data integration technologies that allow chatbots to access patient data in real-time. Healthcare chatbots are AI-powered virtual assistants that provide personalized support to patients and healthcare providers. They are designed to simulate human-like conversation, enabling patients to interact with them as they would with a real person.

Such approaches also raise important questions about the production of knowledge, a concern that AI more broadly is undergoing a reckoning with [19]. Most chatbots (we are not talking about AI-based ones) are rather simple and their main goal is to answer common questions. Hence, when a patient starts asking about a rare condition or names symptoms that a bot was not trained to recognize, it leads to frustration on both sides. A bot doesn’t have an answer and a patient is confused and annoyed as they didn’t get help. So in case you have a simple bot and don’t want your patients to complain about its insufficient knowledge, either invest in a smarter bot or simply add an option to connect with a medical professional for more in-depth advice. Designing chatbot interfaces for medical information involves training the Natural Language Processing (NLP) model on medical terminology.

Comprehending the obstacles encountered by healthcare providers and patients is crucial for customizing the functionalities of the chatbot. This stage guarantees that the medical chatbot solves practical problems and improves the patient experience. In recent years, the healthcare landscape has witnessed a transformative integration of technology, with medical chatbots at the forefront of this evolution. Medical chatbots also referred to as health bots or medical AI chatbots, have become instrumental in reshaping patient engagement and accessibility within the healthcare industry.

Chat-based/Conversational chatbots talk to the user, like another human being, and their goal is to respond correctly to the sentence they have been given. Task-based chatbots perform a specific task such as booking a flight or helping somebody. These chatbots are intelligent in the context of asking for information and understanding the user’s input. Restaurant booking bots and FAQ chatbots chatbot technology in healthcare are examples of Task-based chatbots [34, 35]. Natural Language Processing (NLP), an area of artificial intelligence, explores the manipulation of natural language text or speech by computers. Knowledge of the understanding and use of human language is gathered to develop techniques that will make computers understand and manipulate natural expressions to perform desired tasks [32].

• Importantly, in addition to human-like answers, the perceived human-likeness of chatbots in general can be considered ‘as a likely predictor of users’ trust in chatbots’ (p. 25).
• And the best part is that these actions do not require patients to schedule an appointment or stand in line, waiting for the doctor to respond.
• It can be done via different ways, by asking questions or through a questionnaire that a patient fills in themselves.
• Shum et al. (2018, p. 16) defined CPS (conversation-turns per session) as ‘the average number of conversation-turns between the chatbot and the user in a conversational session’.

The first author worked closely with the chatbot company to determine the type, amount, and format of usage data that needed to be extracted to fulfill the study’s purpose. To protect users’ privacy, all identifiable information (eg, phone number) was removed from the data set by the chatbot company before sending it to the researchers. Moreover, the users consented at the point of registration that researchers were allowed to analyze their usage data for research purposes. The study procedure was approved by the first author’s university’s Institutional Review Board. When a consultation is complete, DoctorBot generates a report detailing potential diagnoses, prediction confidence, treatment options, and which hospital unit (eg, cardiology, urology) to visit (Figure 1, right).

Some ethical issues relative to chatbots would be worth studying like abuse and deception, as people, on some occasions, believe they talk to real humans while they are talking to chatbots. Understanding what the chatbot will offer and what category falls into helps developers pick the algorithms or platforms and tools to build it. Classification based on the knowledge domain considers the knowledge a chatbot can access or the amount of data it is trained upon. Open domain chatbots can talk about general topics and respond appropriately, while closed domain chatbots are focused on a particular knowledge domain and might fail to respond to other questions [34]. Rapid diagnoses by chatbots can erode diagnostic practice, which requires practical wisdom and collaboration between different specialists as well as close communication with patients. HCP expertise relies on the intersubjective circulation of knowledge, that is, a pool of dynamic knowledge and the intersubjective criticism of data, knowledge and processes.

Step 4: Integration with Electronic Health Records (EHR)

In addition to the content, some apps allowed for customization of the user interface by allowing the user to pick their preferred background color and image. Classification based on the goals considers the primary goal chatbots aim to achieve. Informative chatbots are designed to provide the user with information that is stored beforehand or is available from a fixed source, like FAQ chatbots.

Personalized clinical instructions are an important component of a holistic medical approach allowing for the continuity and coordination of care. Answers by Cigna tracks patient incentive programs, provides wellness tips, and enables health coach programs to navigate treatment plans [31]. OrbitaCONNECT provides a virtual health assistant for chronic, pre-, and postvisit care continuity [32]. Furthermore, with Talkspace Alexa skill, users can access mental health assessments and a library of mental health tools [33]. Europe market is estimated to witness the fastest share over the forecast period as there is a rising demand for digital health solutions across Europe as healthcare systems strive to improve access, efficiency, and patient engagement.

Included Studies

For example, the development of the Einstein app as a web-based physics teacher enables interactive learning and evaluations but is still far from being perfect [114]. Given chatbots’ diverse applications in numerous aspects of health care, further research and interdisciplinary collaboration to advance this technology could revolutionize the practice of medicine. With psychiatric disorders affecting at least 35% of patients with cancer, comprehensive cancer care now includes psychosocial support to reduce distress and foster a better quality of life [80]. The first chatbot was designed for individuals with psychological issues [9]; however, they continue to be used for emotional support and psychiatric counseling with their ability to express sympathy and empathy [81]. Health-based chatbots delivered through mobile apps, such as Woebot (Woebot Health, Inc), Youper (Youper, Inc), Wysa (Wysa, Ltd), Replika (Luka, Inc), Unmind (Unmind, Inc), and Shim (Shim, Inc), offer daily emotional support and mental health tracking [26].

With the eHealth chatbot, users submit their symptoms, and the app runs them against a database of thousands of conditions that fit the mold. You can foun additiona information about ai customer service and artificial intelligence and NLP. This is followed by the display of possible diagnoses and the steps the user should take to address the issue – just like a patient symptom tracking tool. This AI chatbot for healthcare has built-in speech recognition and natural language processing to analyze speech and text to produce relevant outputs. To develop a chatbot that engages and provides solutions to users, chatbot developers need to determine what types of chatbots in healthcare would most effectively achieve these goals. Therefore, two things that the chatbot developer needs to consider are the intent of the user and the best help the user needs; then, we can design the right chatbot to address these healthcare chatbot use cases.

The analysis of vocal characteristics is a promising research field that combines AI and clinical medicine. Maor et al [87] reported an independent association between voice signal analysis and hospitalization as well as mortality among patients with heart failure. Furthermore, quantitative voice analysis was shown to be applicable in the diagnosis of neurodegenerative diseases [88] and COVID-19 [89]. Experts who understand the difference between a search engine’s capabilities and the tasks at which generative AI models excel predict the latter will not replace the former. For example, a Lown Institute analysis of Medicare data named the hospitals most likely to unnecessarily implant coronary stents, a procedure whose risks include infection, stroke and even kidney damage. If you Google the facility with the highest inappropriate rate, 53%, you’ll find cautionary information on the first page of the results.

Chatbot Cuts Care-Related Costs

Problems arise when dealing with more complex situations in dynamic environments and managing social conversational practices according to specific contexts and unique communication strategies [4]. To bridge this knowledge gap, we examined one widely deployed chatbot in China—DoctorBot [29]—which had attracted hundreds of thousands of users by the time this study took place. The large-scale deployment of DoctorBot provides us with a unique opportunity to gain an in-depth, empirical understanding of how people use health chatbots in the real world and what barriers hinder the delivery of these novel services. To the best of our knowledge, this is the first study that examined these issues using large-scale, real usage data. More specifically, we took a data-driven approach to analyze the system log of DoctorBot, which consisted of 47,684 consultation sessions initiated by 16,519 users between September 2018 and March 2019. First, we present a detailed analysis of how people use an AI-driven self-diagnosis chatbot, which remains understudied in the health informatics community.

Now, let’s explore the main applications of artificial intelligence chatbots in healthcare in more detail. To facilitate this assessment, we develop and present an evaluative framework that classifies the key characteristics of healthbots. Concerns over the unknown and unintelligible “black boxes” of ML have limited the adoption of NLP-driven chatbot interventions by the medical community, despite the potential they have in increasing and improving access to healthcare. Further, it is unclear how the performance of NLP-driven chatbots should be assessed. The framework proposed as well as the insights gleaned from the review of commercially available healthbot apps will facilitate a greater understanding of how such apps should be evaluated. When chatbots are developed by private healthcare companies, they usually follow the market logic, such as profit maximisation, or at the very least, this dimension is dominant.

While clinicians can enhance patient care through unified hospital communication and centralized storage of patient data. Chatbots ask patients about their current health issue, find matching physicians and dentists, provide available time slots, and can schedule, reschedule, and delete appointments for patients. Chatbots can also be integrated into user’s device calendars to send reminders and updates about medical appointments.

What chatbot building platforms do you recommend to spearhead my bot development?

Input modality, or how the user interacts with the chatbot, was primarily text-based (96%), with seven apps (9%) allowing for spoken/verbal input, and three (4%) allowing for visual input. For the output modality, or how the chatbot interacts with the user, all accessible apps had a text-based interface (98%), with five apps (6%) also allowing spoken/verbal output, and six apps (8%) supporting visual output. Visual output, in this case, included the use of an embodied avatar with modified expressions in response to user input.

Our inclusion criteria were for the studies that used or evaluated chatbots for the purpose of prevention or intervention and for which the evidence showed a demonstrable health impact. We included experimental studies where chatbots were trialed and showed health impacts. We chose not to distinguish between embodied conversational agents and text-based agents, including both these modalities, as well as chatbots with cartoon-based interfaces. As the name implies, prescriptive chatbots are used to provide a therapeutic solution to a patient by learning about their needs and symptoms through a conversation.

In a study using 2 cases, differences in prediction accuracy were shown concerning gender and insurance type for intensive care unit mortality and psychiatric readmissions [103]. On a larger scale, this may exacerbate barriers to health care for minorities or underprivileged individuals, leading to worse health outcomes. Identifying the source of algorithm bias is crucial for addressing health care disparities between various demographic groups and improving data collection. Chatbots can help patients manage their health more effectively, leading to better outcomes and a higher quality of life. These bots can help patients stay on track with their healthcare goals and manage chronic conditions more effectively by providing personalized support and assistance.

Conversely, health consultation chatbots are partially automated proactive decision-making agents that guide the actions of healthcare personnel. In this paper, we conducted both quantitative and qualitative analysis on the system log of a self-diagnosis chatbot, which has been widely deployed in China. To the best of our knowledge, this is the first study examining the use of health chatbots in the real world using a large-scale, heterogeneous data set. We described our general observations of the chatbot’s use, including who used it, how long and how often they used the application, and what health concerns were presented. Furthermore, we analyzed both completed and uncompleted consultation sessions as well as the user feedback to investigate issues that may hinder the effective use of the chatbot.

A thorough research of LLMs is recommended to avoid possible technical issues or lawsuits when implementing a new artificial intelligence chatbot. For example, ChatGPT 4 and ChatGPT 3.5 LLMs are deployed on cloud servers that are located in the US. Hence, per the GDPR law, AI chatbots in the healthcare industry that use these LLMs are forbidden from being used in the EU.

In order to enable a seamless interchange of information about medical questions or symptoms, interactions should be natural and easy to use. In this comprehensive guide, we will explore the step-by-step process of developing and implementing medical chatbot, shedding light on their crucial role in improving patient engagement and healthcare accessibility. Some experts also believe doctors will recommend chatbots to patients with ongoing health issues.

This area holds tremendous potential, as an estimated ≥50% of all patients with cancer have used radiotherapy during the course of their treatment. Early cancer detection can lead to higher survival rates and improved quality of life. Inherited factors are present in 5% to 10% of cancers, including breast, colorectal, prostate, and rare tumor syndromes [62].

• To understand the use of self-diagnosis chatbots in the real world, we took a data-driven approach to analyze the system log of DoctorBot collected between September 2018 and March 2019.
• Layla was designed and developed through community-based participatory research, where the community that would benefit from the chatbot also had a say in its design.
• Conduct regular audits to identify and patch vulnerabilities, ensuring the chatbot’s adherence to legal requirements.
• For example, Mandy is a chatbot that assists health care staff by automating the patient intake process [43].
• With standalone chatbots, businesses have been able to drive their customer support experiences, but it has been marred with flaws, quite expectedly.

If you look up articles about flu symptoms on WebMD, for instance, a chatbot may pop up with information about flu treatment and current outbreaks in your area. Chatbots are integrated into the medical facility database to extract information about suitable physicians, available slots, clinics, and pharmacies  working days. Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine. Capacity’s conversational AI platform enables graceful human handoffs and intuitive task management via a powerful workflow automation suite, robust developer platform, and flexible database that can be deployed anywhere.

Furthermore, there are work-related and ethical standards in different fields, which have been developed through centuries or longer. For example, as Pasquale argued (2020, p. 57), in medical fields, science has made medicine and practices more reliable, and ‘medical boards developed standards to protect patients from quacks and charlatans’. Thus, one should be cautious when providing and marketing applications such as chatbots to patients.

Chatbots experience the Black

Box problem, which is similar to many computing systems programmed using ML that are trained on massive data sets to produce multiple layers of connections. Although they are capable of solving complex problems that are unimaginable by humans, these systems remain highly opaque, and the resulting solutions may be unintuitive. This means that the systems’ behavior is hard to explain by merely looking inside, and understanding exactly how they are programmed is nearly impossible. For both users and developers, transparency becomes an issue, as they are not able to fully understand the solution or intervene to predictably change the chatbot’s behavior [97]. With the novelty and complexity of chatbots, obtaining valid informed consent where patients can make their own health-related risk and benefit assessments becomes problematic [98]. Without sufficient transparency, deciding how certain decisions are made or how errors may occur reduces the reliability of the diagnostic process.

How AI health care chatbots learn from the…

The limitation to the abovementioned studies was that most participants were young adults, most likely because of the platform on which the chatbots were available. In addition, longer follow-up periods with larger and more diverse sample sizes are needed for future studies. Chatbots used for psychological support hold great potential, as individuals are more comfortable disclosing personal information when no judgments are formed, even if users could still discriminate their responses from that of humans [82,85]. From the patient’s perspective, various chatbots have been designed for symptom screening and self-diagnosis. The ability of patients to be directed to urgent referral pathways through early warning signs has been a promising market. Decreased wait times in accessing health care services have been found to correlate with improved patient outcomes and satisfaction [59-61].

A friendly and funny chatbot may work best for a chatbot for new mothers seeking information about their newborns. Still, it may not work for a doctor seeking information about drug dosages or adverse effects. First, the chatbot helps Peter relieve the pressure of his perceived mistake by letting him know it’s not out of the ordinary, which may restore his confidence; then, it provides useful steps to help him deal with it better.

However, the exponential increase in COVID-19 cases has highlighted gaps in the current organizational systems at a global scale [37]. As a result of the number of COVID-19 cases, health care providers have been required to work on the front line, shifting the human resources available for routine care services. Clinical institutions underwent extraordinary reorganizations to accommodate for the surge in COVID-19 cases.

In this way, a patient learns about their condition and its severity and the bot, in return, suggests a treatment plan or even notifies the doctor in case of an emergency. First, chatbots provide a high level of personalization due to the analysis of patient’s data. In this way, a bot suggests relevant recommendations and guidance and receive advice, tailored specifically to their needs and/or condition. Medical chatbots contribute to optimal medication adherence by sending timely reminders and alerts to patients.

Additionally, chatbots can now access electronic health records and other patient data to provide more personalized responses to patient queries. Chatbots have been used in healthcare settings for several years, primarily in customer service roles. They were initially used to provide simple automated responses to common patient questions, such as office hours or medication refill requests. Over time, chatbots in healthcare became more sophisticated, incorporating machine learning and artificial intelligence (AI) to provide more personalized responses. Due to the rapid digital leap caused by the Coronavirus pandemic in health care, there are currently no established ethical principles to evaluate healthcare chatbots. Shum et al. (2018, p. 16) defined CPS (conversation-turns per session) as ‘the average number of conversation-turns between the chatbot and the user in a conversational session’.

Patients can receive support and care remotely, reducing the need for in-person visits and improving access to healthcare services. In particular, we found that the total time spent on the consultation had a significant impact on user experience. If a consultation lasted more than 2 minutes, users tended to rate their experience as negative.

Furthermore, prior work has focused primarily on developing advanced algorithms to improve the accuracy and effectiveness of chatbots’ diagnoses [21,26,27]. Few studies, however, have focused on the use of health chatbots in the real world [25]. More specifically, little is known about the “who, how often, and why” of chatbot use, what barriers and issues exist in using this novel technology, and how to overcome the barriers. This research gap is caused partly by the lack of large-scale deployment of health chatbots [28]. That is, most studies only examined the use of health chatbots in controlled settings rather than in real-world settings, where users interact with chatbots on their own.

Motivational interview–based chatbots have been proposed with promising results, where a significant number of patients showed an increase in their confidence and readiness to quit smoking after 1 week [92]. No studies have been found to assess the effectiveness of chatbots for smoking cessation in terms of ethnic, racial, geographic, or socioeconomic status differences. Creating chatbots with prespecified answers is simple; however, the problem becomes more complex when answers are open.

The graph in Figure 2 thus reflects the maturity of research in the application domains and the presence of research in these domains rather than the quantity of studies that have been conducted. Studies were included if they used or evaluated chatbots for the purpose of prevention or intervention and for which the evidence showed a demonstrable health impact. There are several reasons why chatbots help healthcare organizations elevate their patient care – let’s look at each in a bit of detail. Healthcare organizations all over the world currently face workforce shortages (with COVID-19 being one of the primary factors for that) and in such conditions, the availability of doctors might be in decline. Thus, a 24/7 available digital solution can be a perfect alternative and this is one of the main benefits of chatbots.

Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade.

Deploying chatbots in healthcare leads to cost efficiency by automating routine administrative tasks. This operational streamlining enables healthcare staff to allocate resources effectively, focusing on delivering quality patient care. Chatbots have the potential to transform the way patients understand their medical bills. AI and chatbots can help patients understand their bills by providing detailed explanations of charges, identifying potential errors, and offering guidance on payment options. Case in point, people recently started noticing their conversations with Bard appear in Google’s search results.